本文選題：智能前端　切入點：行人檢測　出處：《大連海事大學(xué)》2017年碩士論文

【摘要】：隨著政治、經(jīng)濟的發(fā)展,各個國家、企業(yè)、個人越來越關(guān)注安防事業(yè)。監(jiān)控系統(tǒng)由最原始的模擬視頻和人眼監(jiān)測到中期的半數(shù)字化存儲再到如今的全數(shù)字化監(jiān)控系統(tǒng),互聯(lián)網(wǎng)的發(fā)展、編解碼算法的升級都功不可沒。而智能前端監(jiān)控系統(tǒng)在監(jiān)控系統(tǒng)中脫穎而出,算法的多樣性需求和前端處理器的飛躍發(fā)展,使得智能前端監(jiān)控系統(tǒng)的廣泛應(yīng)用成為必然。多年來,行人檢測課題的研究持續(xù)不斷。行人檢測算法在電子卡口、無人車行人避讓系統(tǒng)、客流量檢測等應(yīng)用中作為基礎(chǔ)算法有著至關(guān)重要的作用。行人檢測智能前端是帶有行人檢測分析功能的智能前端,不但能夠代替人眼進行監(jiān)查,而且能夠減少傳輸信號所占用的帶寬和存儲資源。本文根據(jù)《安防監(jiān)控視頻實時智能分析設(shè)備技術(shù)要求》設(shè)計了行人檢測智能前端系統(tǒng)的功能和性能要求。通過分析DM8127的優(yōu)勢,確定以DM8127為主處理器的網(wǎng)絡(luò)攝像機作為系統(tǒng)實現(xiàn)的硬件平臺,并分析了行人檢測智能前端的五個模塊,同時結(jié)合系統(tǒng)的軟硬件平臺,選用支持向量機(Support Vector Machine,SVM)和梯度方向直方圖(Histogram of Oriented Gradient,HOG)相結(jié)合的方法作為前端分析模塊的實現(xiàn)方案。在MATLAB上模擬了行人檢測系統(tǒng),包括提取HOG特征模塊、圖像金字塔檢測模塊以及多窗口融合模塊。根據(jù)智能前端行人檢測的實時性和準(zhǔn)確率要求,針對HOG特征的三個缺點給出相應(yīng)的解決方法:1)HOG特征的縮放不變性差。選擇包含不同高度行人的同尺寸圖像數(shù)據(jù)集,通過圖像金字塔檢測原理,設(shè)計三層圖像金字塔,并在各層進行行人檢測,分析HOG特征縮放不變性性能,結(jié)論為64*128大小的檢測窗口可以檢測到行人高度范圍在88像素-128像素內(nèi)的行人。根據(jù)此結(jié)論給出了單層圖像金字塔檢測法。2)HOG特征的特征維度較高。特征維度過高導(dǎo)致提取特征耗時長,檢測速度緩慢,可在保證準(zhǔn)確率的前提下進行算法參數(shù)的適當(dāng)調(diào)整。3)HOG特征對被遮擋的行人,檢測效果較差。挑選只有上半身包含頭肩信息的行人作為部分訓(xùn)練正樣本。檢測到行人后,根據(jù)坐標(biāo)在界面顯示模塊將行人框出,在多尺寸窗口融合技術(shù)的原理上,給出了遞歸的窗口融合算法。仿真后,選用LIBSVM移植到DM8127上用于行人判別,并移植提取HOG特征的算法,實現(xiàn)整個智能監(jiān)控系統(tǒng)并進行測試。實測結(jié)果表明:添加只包含頭肩信息的行人做為訓(xùn)練正樣本,可以有效地解決行人下半身被遮擋的問題;通過調(diào)整HOG特征的提取參數(shù),可以在保證精度符合要求的情況下,有效提高檢測速度;給出的遞歸的窗口融合算法,可以有效地將多個窗口融合;HOG和SVM相結(jié)合的算法移植到DM8127中可以檢測到90%以上的行人。
[Abstract]:With the development of politics and economy, various countries, enterprises and individuals are paying more and more attention to the security cause. The monitoring system is from the most primitive analog video and the human eye to the semi-digital storage in the medium term and then to the full-digital monitoring system now. The development of the Internet, the upgrading of coding and decoding algorithms, and intelligent front-end monitoring system stand out in the monitoring system, the diversity of algorithms and the rapid development of front-end processors, It makes the wide application of intelligent front-end monitoring system inevitable. Over the years, the research on pedestrian detection has continued. Pedestrian detection algorithm in electronic bayonet, unmanned pedestrian avoidance system, The intelligent front end of pedestrian detection is an intelligent front end with the function of pedestrian detection and analysis. Moreover, it can reduce the bandwidth and storage resources of transmission signal. According to the Technical requirements of Real-time Intelligent Analysis equipment for Security Surveillance Video, this paper designs the function and performance requirements of intelligent front-end system for pedestrian detection. By analyzing the advantages of DM8127, this paper analyzes the advantages of this system. The network camera with DM8127 as the main processor is determined as the hardware platform of the system, and the five modules of the intelligent front end of pedestrian detection are analyzed. At the same time, the hardware and software platform of the system is combined with the hardware and software platform of the system. Support vector machine (SVM) and gradient histogram of Oriented histogram (histogram of Oriented gradient histogram) are selected as the implementation of the front-end analysis module. The pedestrian detection system is simulated on MATLAB, including extracting the HOG feature module. Image pyramid detection module and multi-window fusion module. According to the real-time and accuracy requirements of intelligent front-end pedestrian detection, Aiming at the three shortcomings of HOG feature, this paper gives the corresponding solution, that is, the scaling invariance difference of the HOG feature. The same size image data set including different height pedestrians is selected, and the three-layer image pyramid is designed by the principle of image pyramid detection. At the same time, pedestrian detection is carried out in each layer, and the scaling invariance of HOG features is analyzed. Conclusion the detection window with the size of 64m 128 can detect pedestrians whose height ranges from 88 pixels to 128 pixels. According to this conclusion, the feature dimension of pyramid detection method of single-layer image is higher and the characteristic dimension is too high. It takes a long time to extract the feature, The detection speed is slow, and the algorithm parameters can be adjusted properly on the premise of ensuring the accuracy. The detection effect is poor. Only the upper half of the pedestrian with head-shoulder information as part of the training positive sample. After detecting the pedestrian, according to the coordinates display module in the interface to frame the pedestrian, in the principle of multi-size window fusion technology, A recursive window fusion algorithm is presented. After simulation, LIBSVM is transplanted to DM8127 for pedestrian discrimination, and the algorithm for extracting HOG features is transplanted. The whole intelligent monitoring system is implemented and tested. The experimental results show that adding the pedestrian with only head-shoulder information as the training positive sample can effectively solve the problem that the lower body of the pedestrian is occluded, and adjust the extraction parameters of the HOG feature. The proposed recursive window fusion algorithm can effectively transplant the algorithm combining multiple windows with hog and SVM into DM8127 to detect more than 90% of pedestrians.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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